Methods and systems for updating a user interface based on level of user interest

ABSTRACT

A computer-implemented method for providing a personalized interface to a user based on whether the user is serious about making a purchase may include: obtaining customer identification data and customer input data a customer, wherein the customer input data comprises a request from the customer; determining a request status of the customer based on the customer identification data and the customer input data; obtaining customer interface activity data of the customer based on the request status; obtaining customer purchasing data of the customer based on the request status; generating a prediction model based on the customer interface activity data and the customer purchasing data; training the generated prediction model by classifying the customer based on the customer interface activity data and the customer purchasing data; obtaining user identification data and user interface activity data of a user via a user device, the user interface activity data indicating interactive activities between the user and a user interface displayed on the user device; determining a rating of the user to purchase a product based on the user identification data, the user interface activity data, and the prediction model; and providing, to the user, an updated user interface on the user device based on the determined rating.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally todisplaying an updated user interface to a user, and, more particularly,to displaying the updated user interface to the user based on a ratingof the user to purchase a product.

BACKGROUND

Visitors to a retail establishment, be it physical or electronic, mayfall into two general categories: serious customers (e.g., a customerinterested and/or committed to making a purchase) or non-seriouscustomers (e.g., a customer that is browsing and/or not committed tomaking a purchase). Information or actions that may lead or entice anon-serious customer toward being a serious customer may be differentthan information or actions that may be desired by a serious customerwhen making a purchase. Further, it may be difficult to discern one typeof customer from the other, especially when products may be offered forsale electronically with little or no direct interaction with thecustomer, and especially when a merchant has limited or no informationabout the customer. Thus, it may be difficult to develop informationand/or actions for a merchant that are adapted to a customer's level ofinterest.

The present disclosure is directed to overcoming the above-referencedchallenges. The background description provided herein is for thepurpose of generally presenting the context of the disclosure. Unlessotherwise indicated herein, the materials described in this section arenot prior art to the claims in this application and are not admitted tobe prior art, or suggestions of the prior art, by inclusion in thissection.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, methods and systems aredisclosed for providing an updated user interface to a user. Thedisclosed methods and systems may improve a user's experience whensearching for products to buy, and may improve efficiency during theprocess of purchasing products.

In an aspect, a computer-implemented method may include: obtaining, viaone or more processors, customer identification data and customer inputdata of at least one customer, wherein the customer input data includesa request submitted by the at least one customer; determining, via theone or more processors, a request status of the at least one customerbased on the customer identification data and the customer input data;obtaining, via the one or more processors, customer interface activitydata of the at least one customer based on the request status of the atleast one customer; obtaining, via the one or more processors, customerpurchasing data of the at least one customer based on the request statusof the at least one customer; generating, via the one or moreprocessors, a prediction model based on the customer interface activitydata and the customer purchasing data of the at least one customer;training, via the one or more processors, the generated predictionmodel, the training including classifying the at least one customerbased on the customer interface activity data and the customerpurchasing data; obtaining, via the one or more processors, useridentification data and user interface activity data of a user via auser device associated with the user, wherein the user interfaceactivity data indicates one or more interactive activities between theuser and a user interface displayed on the user device; determining, viathe one or more processors, a rating of the user to purchase a productbased on the user identification data, the user interface activity data,and the prediction model; and providing, to the user, an updated userinterface on the user device associated the user based on the determinedrating of the user to purchase the product.

In another aspect, a computer-implemented method may include: obtaining,via one or more processors, customer identification data and customerinput data of at least one customer, wherein the customer input dataincludes a prequalification request submitted by the at least onecustomer; determining, via the one or more processors, aprequalification status of the at least one customer based on thecustomer identification data and the customer input data, wherein theprequalification status of the at least one customer identifies the atleast one customer as a prequalified customer; obtaining, via the one ormore processors, customer interface activity data of the at least onecustomer based on the prequalification status of the at least onecustomer; obtaining, via the one or more processors, customer purchasingdata of the at least one customer based on the prequalification statusof the at least one customer; generating, via the one or moreprocessors, a prediction model based on the customer interface activitydata and the customer purchasing data of the at least one customer;training, via the one or more processors, the generated predictionmodel, the training including classifying the at least one customerbased on the customer interface activity data and the customerpurchasing data; obtaining, via the one or more processors, useridentification data and user interface activity data of a user via auser device associated with the user, wherein the user interfaceactivity data indicates one or more interactive activities between theuser and a user interface including one or more original layoutsdisplayed on the user device; determining, via the one or moreprocessors, a rating of the user to purchase a product based on the useridentification data, the user interface activity data, and theprediction model; and providing, to the user, an updated user interfaceon the device associated the user based on the rating of the user topurchase the product, wherein the updated user interface includes one ormore adjusted layouts that are different from the one or more originallayouts.

In yet another aspect, a computer system for providing a vehiclerecommendation to a user may include a memory storing instructions, andone or more processors configured to execute the instructions to performoperations. The operations may include: obtaining customeridentification data and customer input data of at least one customer,wherein the customer input data includes a request submitted by the atleast one customer; determining a request status of the at least onecustomer based on the customer identification data and the customerinput data; obtaining customer interface activity data of the at leastone customer based on the request status of the at least one customer;obtaining customer purchasing data of the at least one customer based onthe request status of the at least one customer; generating a predictionmodel based on the customer interface activity data and the customerpurchasing data of the at least one customer; training the generatedprediction model, the training including classifying the at least onecustomer based on the customer interface activity data and the customerpurchasing data; obtaining user identification data and user interfaceactivity data of the user via a user device associated with a user,wherein the user interface activity data indicates one or moreinteractive activities between the user and a user interface displayedon the user device; determining a rating of the user to purchase aproduct based on the user identification data, the user interfaceactivity data, and the prediction model; and providing, to the user, anupdated user interface on the user device associated the user based onthe determined rating of the user to purchase the product.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts an exemplary system infrastructure, according to one ormore embodiments.

FIG. 2 depicts a flowchart of an exemplary method for providing anupdated user interface to a user, according to one or more embodiments.

FIG. 3 depicts a flowchart of an exemplary method for providing apersonalized electronic store experience to a visitor, according to oneor more embodiments.

FIG. 4 depicts an example of a computing device, according to one ormore embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection. Both the foregoing general description and the followingdetailed description are exemplary and explanatory only and are notrestrictive of the features, as claimed.

In this disclosure, the term “based on” means “based at least in parton.” The singular forms “a,” “an,” and “the” include plural referentsunless the context dictates otherwise. The term “exemplary” is used inthe sense of “example” rather than “ideal.” The terms “comprises,”“comprising,” “includes,” “including,” or other variations thereof, areintended to cover a non-exclusive inclusion such that a process, method,or product that comprises a list of elements does not necessarilyinclude only those elements, but may include other elements notexpressly listed or inherent to such a process, method, article, orapparatus. Relative terms, such as, “substantially” and “generally,” areused to indicate a possible variation of ±10% of a stated or understoodvalue. The terms “customer”, “buyer”, “purchaser”, “user”, “visitor”,and the like generally encompass individuals or entities that areviewing products for sale, contemplating a purchase of a product, arepurchasing a product, or have purchased a product. The term “product”generally encompasses goods, services, or combinations thereof.

An electronic merchant may offer products for sale electronically, e.g.,via a website page or an electronic application, or the like. Inaddition to offers, an electronic store may include content designed to,for example, inform a visitor about the offered products, entice thevisitor to make a purchase or perform another desired action such assign up for a membership or subscription, fill out a questionnaire, seekfinancial prequalification associated with a purchase, or the like.However, different types of customers may be more receptive to and/ortargeted by different types of content, and thus it may be difficult todesign an electronic store that is adapted to any particular customer.

For example, a serious purchaser seeking to buy a particular car may berelatively more interested in and/or receptive to information onobtaining financing, and relatively less interested and/or receptive toadvertising material for various vehicles. In another example, anon-serious purchaser browsing a merchant's inventory may be relativelyless interested in and/or receptive to information on obtainingfinancing, and relatively more interested and/or receptive toadvertising material for various vehicles. Displaying content that aparticular visitor may be less interested in and/or less receptive tomay negatively impact the visitor's experience and/or result in visitordisengagement.

Moreover, an electronic store may permit visitors to browse, view,and/or purchase products with at least some anonymity, and thus it maybe difficult to even discern what type of customer a particular visitormay be. Therefore, there is a need for techniques for determining whattype of customer a visitor to an electronic store may be, and fortailoring the visitors experience with the electronic store to theirdetermined customer type.

In an exemplary use case, an electronic merchant may employ a systemconfigured to determine a customer type for a visitor, and to tailor avisitor experience of an electronic store to the visitor based on thedetermined customer type. In some embodiments, in response to thevisitor first accessing the electronic store, the system may configurethe electronic store to provide a neutral customer experience, e.g., notgeared toward either a serious customer or a non-serious customer. Inother embodiments, the electronic store may have an initialconfiguration for a serious customer experience, a non-serious customerexperience, or any suitable type of customer experience. As the visitorinteracts with the electronic store, e.g., via selecting options,navigating to various areas of the electronic store, searching for orviewing products, or the like, the system may track the visitor'sinteractions with the electronic store.

Based on the tracked interactions, the system may determine whether thevisitor is more likely a serious customer or a non-serious customer, andmay determine a customer type for the visitor based on the determinedlikelihood. The system may configure the electronic store based on thedetermined customer type. For example, the system may prominentlyarrange or add advertising content in response to determining that thevisitor is a non-serious customer, and may prominently arrange or addinformation or services related to financing for a purchase in responseto determining that the visitor is a serious customer. It should beunderstood that these examples of configuration changes are illustrativeonly, and that various types of content may be added, removed,rearranged, highlighted, or the like when configuring an electronicstore based on a determined customer type.

Any suitable technique and/or criteria for determining a customer typeof a visitor based on the tracked interactions of the visitor on theelectronic store may be used. Some interactions, such as adding aproduct to a shopping cart, initiating a purchase, or the like may behighly indicative that a visitor is a serious customer. However, suchinteractions may not occur until late in the process of a visitorvisiting the electronic store. Other interactions may also be indicativethat a visitor is a serious customer, such as performing searches for aparticular product, navigating to particular areas of the electronicstore, selecting options for a particular product, or the like.Similarly, some interactions may be indicative that a visitor is anon-serious customer, such as browsing a wide variety of products,navigating to particular areas of the electronic store, or the like.

In some embodiments, a customer type for a visitor may be determinedbased on one or more interactions of the visitor matching one or moreinteractions associated with a particular customer type. However, theremay be overlap between interactions associated with serious andnon-serious customers. Further, serious customers may interact with theelectronic store in ways associated with non-serious customers andvice-versa. Miss-determination of the customer type for a visitor mayresult in miss-tailoring of the visitor's experience with the electronicstore.

Historical interactions of prior visitors who did make purchases, andthus may be considered serious customers, and/or of prior visitors whodid not make purchases, and thus may be considered non-seriouscustomers, may be used to form a model of interactions for a seriousand/or non-serious customer that may be compared against the visitor'stracked interactions. However, some serious customers may not have endedup making a purchase, and some non-serious customers may have ended upmaking a purchase, and thus a model based on historical purchasedecisions alone may result in false positive and/or false negativedeterminations of a serious customer.

Other criteria may be used to identify serious customers in order togenerate a model of interactions for a serious customer. For example,obtaining financial or loan prequalification by the customer isgenerally a perquisite to completing a transaction for the purchase of avehicle, real estate, etc. A customer that has a prequalification may beconsidered a serious customer whether or not that customer ultimatelymade a purchase or not. Thus, in some embodiments, the system mayidentify historical customers that have obtained such aprequalification, and generate a model of interactions for a seriouscustomer based on tracked interactions of the identified customers.

However, the interaction behavior of a customer may change once thecustomer has obtained a prequalification. Thus, the full trackedinteractions of the identified customers may not be indicative of theinteractions of a serious customer prior to obtaining aprequalification. Thus, in some embodiments, the system may extract aportion of the tracked history of each identified customer, and use theextracted portions to generate the model. The extracted portion mayinclude, for example, a portion of the tracked interactions prior to thecustomer obtaining the prequalification, a portion corresponding to apredetermined period of time such as a period of time corresponding tothe customer's first visit to the electronic store, or the like.

Any suitable technique for generating a model of customer interactionsmay be used. In some embodiments, tracked interactions of customers of aparticular customer type are tallied together to identify one or moreinteractions that a commonly associated with customers of that type.However, a tally of interactions may not consider an order, pattern,frequency, speed, relationships or correlations between interactions, orother factors that may be indicative of a customer type. In someembodiments, the system may employ one or more machine learningtechniques to generate one or more models of interactions indicative ofa particular type of customer.

Any suitable machine learning techniques may be used. In one example,the system may employ one or more machine learning algorithms.Generally, employing a machine learning algorithm encompasses training amachine learning model so as to be configured to make a classification,and then providing a sample to the trained machine learning model inorder to determine a classification for the sample. Training datagenerally includes one or more historical samples and ground truthclassifications for the historical samples. By training the machinelearning model, the machine learning model may learn correlations and/orrelationships between various aspects of the historical samples and theassociated ground truths. In an exemplary use case, training data for amachine learning model may include (i) the portions of trackedinteractions for customers identified to have a prequalification andfrom a period of time prior to the customers obtaining theprequalification, and (ii) a ground truth classification for theidentified customers having a prequalification as serious customers. Anysuitable training data may be used. Any suitable machine learning modelmay be used. In some embodiments, the machine learning model includes aneural network that includes one or more layers of neurons. The trainedmachine learning model may thus determine a classification, e.g., acustomer type, for a given set of tracked interactions of a visitor tothe electronic store. The system may then configure the electronic storebased on the determined customer type in order to tailor the visitor'sexperience on the electronic store to the visitor.

In another exemplary use case of training a machine learning model, amerchant may provide an electronic store offering a variety of vehiclesfor sale. Visitors to the electronic store may search the inventory ofthe electronic store, and may specify one or more aspects of a desiredvehicle such as make, model, year, price, engine type, seats, etc.Generally, obtaining a loan or financing preauthorization may be aprerequisite to completing a transaction to purchase a vehicle. Overtime, a system for classifying visitors and tailoring the configurationof the electronic store may track user interactions with the electronicstore as well as whether the visitors obtained such preauthorization.Visitors identified as having obtained the preauthorization may beclassified as serious customers, and portions of the trackedinteractions of the serious customers prior to obtaining thepreauthorization may be used to generate a model of interactionsassociated with a serious customer.

In a further exemplary use case of tailoring a visitor's experience onan electronic store, a visitor that is a serious customer may access theelectronic store of the merchant. The visitor may interact with theelectronic store in one or more ways such as, for example, searching fora particular vehicle and/or specifying one or more aspects of thevehicle, viewing detailed information about a particular vehicle,navigating to a particular portion of the electronic store, hovering amouse curser or the like over a particular item and/or portion of theelectronic store, revisiting the electronic store or a portion thereofmultiple times, or the like. The system may track the visitor'sinteraction and over time, or after a predetermined extent of time forthe visit, or after a predetermined number of interactions, or inreal-time, or in response to any suitable criteria, the system may usethe tracked interactions with a model of interactions associated with aserious customer to determine whether the customer is a seriouscustomer. In response to determining that the visitor is a seriouscustomer, the system may one or more of add, remove, or rearrangecontent on the electronic store. For example, the system may removeadvertising content, may add content associated with obtaining aprequalification, or may rearrange content associated with time-windowedpurchase incentives.

In a further exemplary use case of tailoring a visitor's experience onan electronic store, a visitor that is a non-serious customer may accessthe electronic store of the merchant. The visitor may interact with theelectronic store in one or more ways such as, for example, viewing aplurality of different vehicles, quickly moving from page to page,navigating to a particular portion of the electronic store, hovering amouse curser or the like over a particular item and/or portion of theelectronic store, or the like. The system may track the visitor'sinteraction and over time, or after a predetermined extent of time forthe visit, or after a predetermined number of interactions, or inreal-time, or in response to any suitable criteria, the system may usethe tracked interactions with model of interactions associated with aserious customer to determine whether the customer is a seriouscustomer. In response to determining that the visitor is a non-seriouscustomer, the system may one or more of add, remove, or rearrangecontent on the electronic store. For example, the system may addadvertising content, may remove content associated with obtaining aprequalification, or may rearrange content associated with time-windowedpurchase incentives.

FIG. 1 is a diagram depicting an example of a system environment 100according to one or more embodiments of the present disclosure. Thesystem environment 100 may include a computer system 110, a network 130,one or more resources 140 for collecting data (e.g., user identificationdata), one or more customer devices 150 associated with a historicalcustomer, and a user device 160 associated with a user, i.e. a currentcustomer. A historical customer is a person that has had a previousinteraction with an electronic store associated with the systemenvironment 100. The previous interaction may or may not include apurchase. In some embodiments, a customer device 150 may be used toaccess the electronic store, and thus may be considered a user device150. The one or more resources 140 for collecting data may includefinancial services providers 141, on-line resources 142, or otherthird-party entities 143. These components may be in communication withone another via network 130.

The computer system 110 may have one or more processors configured toperform methods described in this disclosure. The computer system 110may include one or more modules, models, or engines. The one or moremodules, models, or engines may include an algorithm model 112, anotification engine 114, a data processing module 116, aprequalification status module 118, a user identification module 120,and/or an interface/API module 122, which may each be softwarecomponents stored in the computer system 110. The computer system 110may be configured to utilize one or more modules, models, or engineswhen performing various methods described in this disclosure. In someexamples, the computer system 110 may have a cloud computing platformwith scalable resources for computation and/or data storage, and may runone or more applications on the cloud computing platform to performvarious computer-implemented methods described in this disclosure. Insome embodiments, some of the one or more modules, models, or enginesmay be combined to form fewer modules, models, or engines. In someembodiments, some of the one or more modules, models, or engines may beseparated into separate, more numerous modules, models, or engines. Insome embodiments, some of the one or more modules, models, or enginesmay be removed while others may be added.

The algorithm model 112 may be a plurality of algorithm models. Thealgorithm model 112 may include a trained machine learning model (e.g.,a k-nearest neighbors algorithm). Details of algorithm model 112 and thetrained machine learning model are described elsewhere herein. Thenotification engine 114 may be configured to generate and communicate(e.g., transmit) one or more notifications (e.g., an updated userinterface) to a customer device 150 and/or a user device 160 or to oneor more resources 140 through the network 130. The data processingmodule 116 may be configured to monitor, track, clean, process, orstandardize data (e.g., customer interface activity data,prequalification status, customer purchasing data, user identificationdata, or user interface activity data) received by the computer system110. One or more algorithms may be used to clean, process, orstandardize the data. The prequalification status module 118 may beconfigured to monitor, track, determine, or store a prequalificationstatus of at least one customer. Such prequalification status may bedetermined by one or more algorithms provided by one or more resources140. The user identification module 120 may manage user identificationfor each user accessing the computer system 110. In one implementation,the user identification associated with each user may be stored to, andretrieved from, one or more components of data storage associated withthe computer system 110 or one or more resources 140. The interface/APImodule 122 may allow the user to interact with one or more modules,models, or engines of the computer system 110. The interface/API mayalso help track the customer interface activity data, prequalificationstatus, customer purchasing data, user identification data, or userinterface activity data.

Computer system 110 may be configured to receive data from othercomponents (e.g., one or more resources 140, customer device 150 or userdevice 160) of the system environment 100 via network 130. Computersystem 110 may further be configured to utilize the received data byinputting the received data into the algorithm model 112 to produce aresult (e.g., a rating and/or classification of the customer's interestand or commitment to purchase a product). Information indicating theresult may be transmitted to customer device 150, user device 160 and/orone or more resources 140 over network 130. In some examples, thecomputer system 110 may be referred to as a server system that providesa service including providing the information indicating the receiveddata and/or the result to one or more resources 140, customer device150, or user device 160.

Network 130 may be any suitable network or combination of networks andmay support any appropriate protocol suitable for communication of datato and from the computer system 110 and between various other componentsin the system environment 100. Network 130 may include a public network(e.g., the Internet), a private network (e.g., a network within anorganization), or a combination of public and/or private networks.Network 130 may be configured to provide communication between variouscomponents depicted in FIG. 1. Network 130 may comprise one or morenetworks that connect devices and/or components in the network layout toallow communication between the devices and/or components. For example,the network 130 may be implemented as the Internet, a wireless network,a wired network (e.g., Ethernet), a local area network (LAN), a WideArea Network (WANs), Bluetooth, Near Field Communication (NFC), or anyother type of network that provides communications between one or morecomponents of the network layout. In some embodiments, network 130 maybe implemented using cell and/or pager networks, satellite, licensedradio, or a combination of licensed and unlicensed radio.

Financial services providers 141 may be an entity such as a bank, creditcard issuer, merchant services providers, or other type of financialservice entity. In some examples, financial services providers 141 mayinclude one or more merchant services providers that provide merchantswith the ability to accept electronic payments, such as payments usingcredit cards and debit cards. Therefore, financial services providers141 may collect and store data pertaining to transactions occurring atthe merchants. In some embodiments, financial services providers 141 mayprovide a platform (e.g., an app on a customer device 150 or user device160) that a user or a customer can interact with. Such interactions mayprovide data (e.g., customer interface activity data or user interfaceactivity data) that may be analyzed or used in the method disclosedherein. The financial services providers 141 may include one or moredatabases to store any information related to the user or the customer.The financial services providers 141 may provide services associatedwith product transactions. The financial services providers 141 may alsocollect or store prequalification stats of the customer or the user.

Online resources 142 may include webpage, e-mail, apps, or socialnetworking sites. Online resources 142 may be provided by manufacturers,vehicle dealers, retailers, consumer promotion agencies, and otherentities. For example, online resources 142 may include a webpage thatusers can access to select, buy, or sell a product. Online resources 142may include other computer systems, such as web servers, that areaccessible by computer system 110.

Other third-party entities 143 may be any entity that is not a financialservices provider 141 or online resources 142. Other third-partyentities 143 may include merchants that may each be an entity thatprovides products. The term “product,” in the context of productsoffered by a merchant, encompasses both goods and services, as well asproducts that are a combination of goods and services. A merchant maybe, for example, a retailer, a vehicle dealer, a grocery store, anentertainment venue, a service provider, a restaurant, a bar, anon-profit organization, or other type of entity that provides productsthat a consumer may consume. A merchant may have one or more venues thata consumer may physically visit in order to obtain the products (goodsor services) offered by the merchant. In some embodiments, otherthird-party entities 143 may provide a platform (e.g., an app on acustomer device 150 or user device 160) with which a user or a customercan interact. Such interactions may provide data (e.g., user interfaceactivity data) that may be analyzed or used in the method disclosedherein.

The financial services providers 141, the online resources 142, or anyother type of third-party entity 143 may each include one or morecomputer systems configured to gather, process, transmit, and/or receivedata. In general, whenever any of financial services providers 141, theonline resources 142, or any other type of third-party entity 143 isdescribed as performing an operation of gathering, processing,transmitting, or receiving data, it is understood that such operationsmay be performed by a computer system thereof. In general, a computersystem may include one or more computing devices, as described inconnection with FIG. 4 below.

User device 160 and/or customer device 150 may operate a client program,also referred to as a user/customer application, respectively, used tocommunicate with the computer system 110. The client program may beprovided by the financial services providers 141, the online resources142, or any other type of third-party entity 143. This client programmay be used to accept user/customer input or provide information (e.g.,customer interface activity data or user identification data) to thecomputer system 110 and to receive information from the computer system110. In some examples, the client program may be a mobile applicationthat is run on user device 160 or customer device 150. User device 160and/or customer device 150 may be a mobile device (e.g., smartphone,tablet, pager, personal digital assistant (PDA)), a computer (e.g.,laptop computer, desktop computer, server), or a wearable device (e.g.,smart watch). User device 160 and/or customer device 150 may alsoinclude any other media content player, for example, a set-top box, atelevision set, a video game system, or any electronic device capable ofproviding or rendering data. User device 160 and/or customer device 150may optionally be portable. The User device 160 and/or customer device150 may be handheld. User device 160 and/or customer device 150 may be anetwork device capable of connecting to a network, such as network 130,or other networks such as a local area network (LAN), wide area network(WAN) such as the Internet, a telecommunications network, a datanetwork, or any other type of network.

Computer system 110 may be part of an entity 105, which may be any typeof company, organization, or institution. In some examples, entity 105may be a financial services provider 141. In such examples, the computersystem 110 may have access to data pertaining to transactions through aprivate network within the entity 105. For example, if the entity 105 isa card issuer, entity 105 may collect and store data involving a creditcard or debit card issued by the entity 105. In such examples, thecomputer system 110 may still receive data from other financial servicesproviders 141.

FIG. 2 is a flowchart illustrating a method for providing an updateduser interface to the user, according to one or more embodiments of thepresent disclosure. The method may be performed by computer system 110.

Step 201 may include obtaining, via one or more processors, customeridentification data and customer input data of at least one customer,e.g., a record associated with at least one historical customer. Thecustomer identification data may include at least one of a name, apassword, a numerical identifier associated with the at least onehistorical customer, or the like. For example, the customeridentification data may include a social security number of the at leastone historical customer. The identification data may further oralternatively include an actual name, contact information (e.g.,address, phone numbers, e-mail addresses, etc.), a social securitynumber, and/or additional information pertaining to the at least onehistorical customer. The additional information may include, e.g.,customer preference information, demographic information (e.g., age,gender, marital status, income level, educational background, number ofchildren in household, etc.), employment, and/or other data related tothe at least one customer. The customer input data may further oralternatively include a request submitted by the at least one historicalcustomer. The request may be a prequalification request associated withthe at least one historical customer. The request may include anyinformation indicative of and/or regarding purchasing a product,including a social security number or a credit score of the at least onehistorical customer, or the like.

Step 202 may include determining, via the one or more processors, arequest status of the at least one historical customer based on thecustomer identification data and the customer input data. The requeststatus of the at least one historical customer may indicate whether theat least one customer is prequalified to purchase one or more productswithin a specified price range based on the customer identification dataand/or customer input data of the user. For example, theprequalification status may indicate that the historical customer isprequalified to purchase a vehicle from $20,000.00 to $30,000.00 basedon a credit score of 700.

Step 203 may include obtaining, via the one or more processors, customerinterface activity data of the at least one historical customer based onthe request status of the at least one historical customer. The customerinterface activity data may indicate one or more interactive activitiesbetween the at least one historical customer and a customer interfacedisplayed on the customer device 150 associated with the at least onehistorical customer. The customer interface activity data may furtherindicate at least a level of interaction of one of the one or moreinteractive activities between the at least one historical customer andthe customer interface displayed on the customer device 150 associatedwith the at least one historical customer. The one or more interactiveactivities may include at least one of an action of clicking a link, anaction of typing a search term, or an action of selecting a filterperformed by the user. The customer device 150 may be capable ofaccepting customer inputs via one or more interactive components of thecustomer device 150, such as a keyboard, button, mouse, touchscreen,touchpad, joystick, trackball, camera, microphone, or motion sensorinput. For instance, the customer may type a product name via a keyboardprovided on the display of the device associated with the customer. Inanother example, the historical customer may click on one or moreselections associated with a product displayed on a display of thecustomer device. The one or more selections may be in a form of a link,button, or hyperlink.

Step 204 may include obtaining, via the one or more processors, customerpurchasing data of the at least one historical customer based on therequest status of the at least one customer. The customer purchasingdata may include at least one of a confirmation of purchasing theproduct, a time of purchasing the product, or a location of purchasingthe product. The customer purchasing data may include any informationregarding a transaction associated with purchasing a product, forexample, a customer identifier, contact information (e.g., address,phone numbers, e-mail addresses, etc.), demographic information (e.g.,age, gender, marital status, income level, educational background,number of children in household, etc.), customer preferences(preferences or reviews regarding favorite products and/or services,favorite department stores, etc.), a transaction amount, and previoustransaction information. The previous transaction information mayinclude a time of a prior transaction, a location of a priortransaction, spending profile of a historical customer, past spendinglevels on goods sold by various manufacturers or merchants, a frequencyof shopping by the historical customer at one or more merchants, howmuch the historical customer spends in an average transaction, how muchthe historical customer has spent on a particular product, how often thehistorical customer shops in a particular store or kind of merchant, anestimated profit margin on goods previously purchased, or online oroffline stores at which the customer has purchased items. The customerpurchasing data may include safety or recall information regarding theproduct, upgrade or repair information specific to the product, possiblesubstitute or compatible items for the products, and so forth. Theproduct may include a vehicle. In some embodiments, the product may beany item (e.g., a property) or service (e.g., credit card application)provided by a merchant or a financial services provider 141.

Step 205 may include generating, via the one or more processors, aprediction model based on the customer interface activity data and thecustomer purchasing data of the at least one historical customer. Anysuitable type of model, and any suitable technique for generating themodel may be used. Step 206 may include training, via the one or moreprocessors, the generated prediction model. The training may includeclassifying the at least one historical customer based on the customerinterface activity data and the customer purchasing data. For example,the at least one historical customer may be classified as a seriouscustomer based on the request status of the at least one historicalcustomer.

The prediction model may include a trained machine learningalgorithm/model. The trained machine learning algorithm may include aregression-based model that accepts the customer identification data,customer input data, customer interface activity data, or customerpurchasing data as input data. The trained machine learning algorithmmay be part of the algorithm model 112. The trained machine learningalgorithm may be of any suitable form, and may include, for example, aneural network. A neural network may be software representing a humanneural system (e.g., cognitive system). A neural network may include aseries of layers termed “neurons” or “nodes.” A neural network maycomprise an input layer, to which data is presented, one or moreinternal layers, and an output layer. The number of neurons in eachlayer may be related to the complexity of a problem to be solved. Inputneurons may receive data being presented and then transmit the data tothe first internal layer through the connections' weight. The trainedmachine learning algorithm may include a convolutional neural network(CNN), a deep neural network, a recurrent neural network (RNN), or anyother type of neural network.

The machine learning algorithm may be trained by supervised,unsupervised, or semi-supervised learning using training sets comprisingdata of types similar to the type of data used as the model input. Forexample, the training set used to train the model may include anycombination of the following: the customer identification data, customerinput data, customer interface activity data, or customer purchasingdata, or any other data. Accordingly, the machine learning model may betrained to map input variables (e.g., identification data and/orinterface activity data) to a quantity or value of a rating of theuser's interest, seriousness, commitment or the like to purchase aproduct. That is, the machine learning model may be trained to determinea quantity or value of a rating associated with the user's seriousnessto purchase a product as a function of various input variables.

The machine learning algorithm may include a classification algorithm.The classification algorithm may include linear classifiers (e.g.,logistic regression, Naïve Bayes classifier), support vector machines,quadratic classifiers, Kernel estimation (e.g., k-nearest neighbor),boosting (e.g., gradient boosting machines), or decision trees (e.g.,random forests). The k-nearest neighbors algorithm (k-NN) may include atraining phase including storing the feature vectors and class labels ofthe training samples. The k-nearest neighbors algorithm (k-NN) mayinclude a classification phase including k as a user-defined constantand an unlabeled vector (a query or test point) classified by assigningthe label which is most frequent among the k training samples nearest tothat query point. A commonly used distance metric for continuousvariables is Euclidean distance.

In some instances the historical customer may return to an electronicstore, and thus may be considered a user. In some instances, a personother than a historical customer may visit the electronic store, andthus may be considered a user. Step 207 may include obtaining, via theone or more processors, user identification data and user interfaceactivity data of a user via the user device 160 associated with theuser. The user identification data may include at least one of a name, apassword, or a social security number of the user. The identificationdata may further or alternatively include an actual name, contactinformation (e.g., address, phone numbers, e-mail addresses, etc.), asocial security number, and/or additional information pertaining to theat least one user. The additional information may include, e.g., userpreference information, demographic information (e.g., age, gender,marital status, income level, educational background, number of childrenin household, etc.), employment, and/or other data related to the atleast one customer. In some embodiments, the user may withhold at leasta portion of the user identification data, and the user may be at leastpartially anonymous.

The user interface activity data indicates one or more interactiveactivities between the user and a user interface displayed on the userdevice 160. The user may or may not be the at least one customer. Theuser interface activity data may indicate one or more interactiveactivities between the user and a user interface displayed on a userdevice 160 associated with the user. The user interface activity datamay further indicate at least a level of interaction of one of the oneor more interactive activities between the user and the user interfacedisplayed on the user device 160 associated with the user. The one ormore interactive activities include at least one of an action ofclicking a link, an action of typing a search term, or an action ofselecting a filter performed by the user. The user device 160 may becapable of accepting user inputs via one or more interactive componentsof the user device 160, such as a keyboard, button, mouse, touchscreen,touchpad, joystick, trackball, camera, microphone, or motion sensorinput. For instance, the user may type a product name via a keyboardprovided on the display of the device associated with the user. Inanother example, the user may click on one or more selections associatedwith a product displayed on a display of the user device 160. The one ormore selections may be in a form of a link, button, or hyperlink.

Step 208 may include determining, via the one or more processors, arating of the user's interest and/or seriousness in purchasing a productbased on the user identification data, the user interface activity data,and/or the prediction model. The product may be a vehicle. Step 209 mayinclude providing, to the user, an updated user interface on the userdevice 160 associated with the user based on the determined rating ofthe user's seriousness to purchase the product. The updated userinterface may be configured to be displayed on the device associatedwith the user (user device 160). The updated user interface may includeone or more elements and/or an arrangement of the one or more elementsselected based on the determined rating of the user's seriousness. Inother words, the updated user interface may be different for differentusers with different seriousness ratings, or may be different for a userat different times based on a determination that the user's seriousnesshas changed.

The user interface may include an interactive feature configured toenable the user to accept or reject a vehicle recommendation. The userinterface may include information regarding the vehicle recommendation.The user interface may include information and/or selectable optionsrelating to financial information or qualifications. The user interfacemay include an arrangement that more prominently features some elementsand less prominently features others. In some embodiments, the userinterface may be configured to be displayed on a display screen of auser device associated with the user (e.g., user device 160). The userdevice 160 may be capable of accepting inputs of a user via one or moreinteractive components of the user device 160, such as a keyboard,button, mouse, touchscreen, touchpad, joystick, trackball, camera,microphone, or motion sensor.

FIG. 3 is a flowchart illustrating another exemplary method forproviding an updated user interface to a user, according to one or moreembodiments of the present disclosure. The method may be performed bycomputer system 110.

Step 301 may include obtaining, via one or more processors, customeridentification data and customer input data of at least one historicalcustomer. The customer input data may include a prequalification requestsubmitted by the at least one historical customer. The customeridentification data may include at least one of a name, a password, or asocial security number of the at least one historical customer. Detailsof the customer input data and the customer identification data aredescribed elsewhere herein.

Step 302 may include determining, via the one or more processors, aprequalification status of the at least one historical customer based onthe customer identification data and the customer input data. Theprequalification status of the at least one historical customer mayidentify the at least one historical customer as a prequalifiedcustomer. For example, the prequalification status may indicate whetherthe historical customer is prequalified to purchase one or more productswithin a specified price range based on the credit score of the user.For example, the prequalification status may indicate that thehistorical customer is prequalified to purchase a vehicle from$20,000.00 to $30,000.00 based on a credit score of 700. Theprequalification status may also show whether the historical customer isprequalified to apply for financial cards (e.g., credit card) based onthe credit score of the historical customer.

Step 303 may include obtaining, via the one or more processors, customerinterface activity data of the at least one historical customer based onthe prequalification status of the at least one customer. For example,the prequalification status may indicate that the historical customer isprequalified to purchase a vehicle from $20,000.00 to $30,000.00 basedon a credit score of 700. If the historical customer is prequalified topurchase the vehicle, the customer interface activity data may beobtained. Details of the customer interface activity data are describedelsewhere herein.

Step 304 may include obtaining, via the one or more processors, customerpurchasing data of the at least one customer based on theprequalification status of the at least one customer. For example, theprequalification status may indicate that the historical customer isprequalified to purchase a vehicle from $20,000.00 to $30,000.00 basedon a credit score of 700. If the historical customer is prequalified topurchase the vehicle, the customer purchasing data may be obtained.Customer purchasing data may include, for example, whether the at leastone customer completed a purchase and of what. Details of the customerpurchasing data are described elsewhere herein.

Step 305 may include generating, via the one or more processors, aprediction model based on the customer interface activity data and thecustomer purchasing data of the at least one historical customer. Step306 may include training, via the one or more processors, the generatedprediction model. The training may include classifying the at least onehistorical customer based on the customer interface activity data andthe customer purchasing data as described elsewhere herein.

Step 307 may include obtaining, via the one or more processors, useridentification data and user interface activity data of a user via auser device associated with the user. The user interface activity datamay indicate one or more interactive activities between the user and auser interface including one or more original layouts displayed on theuser device. For example, in some embodiments, the user may access anelectronic store and encounter a first, original layout, and the user'sinteractions with this first original layout may be tracked.

Step 308 may include determining, via the one or more processors, arating of the user and/or the user's interest or seriousness to purchasea product based on the user identification data, the user interfaceactivity data, and/or the prediction model.

Step 309 may include providing, to the user, an updated user interfaceon the device associated the user based on the rating. The updated userinterface may include one or more adjusted layouts that are differentfrom the one or more original layouts. In other words, the rating mayindicate that the first original layout is not matched to the user'sinterest or seriousness to purchase the product, and thus the firstoriginal layout may be adjusted or updated to more closely align withthe user's rating. This may include adding, removing, or rearrangingelements of the layout, as discussed in more detail elsewhere.

At any stage of updating a user interface, the method may furtherinclude storing any information, including, for example, the customerpurchasing data, the customer interface activity data, the userinterface activity data, etc., for subsequent analysis. The stored datamay have an expiration period. The expiration period may be at least 1day, 1 week, 1 month, 1 quarter, 1 year, or longer. In otherembodiments, the expiration period may be at most 1 year, 1 quarter, 1month, 1 week, 1 day, or shorter. The subsequent analysis may includeanalyzing the information. The stored data may also be one of the one ormore variables used in training a trained machine learning model.Details of the trained machine learning model are described elsewhereherein.

Additional aspects of this disclosure are illustrated in furtherembodiments below.

In an exemplary embodiment, a method for personalizing a visitorexperience of an electronic store includes: receiving an indication thata visitor has accessed an electronic store; tracking interactions of thevisitor with the electronic store; based on the tracked interactions ofthe visitor, determining whether the visitor is a serious customer or anon-serious customer; and altering the electronic store based on thedetermination. In some embodiments, the determining is performed inresponse to one or more of tracking of the visitor's interactions over apredetermined period of time, tracking at least a predetermined quantityof interactions, or identifying a predetermined interaction and/orpattern of interactions. In some embodiments, an interaction mayinclude, for example, a mouse movement, a mouse hover, a click, aselection, a view of a website page or portion of the electronic store,a search, combinations thereof and/or patterns formed thereby. In someembodiments, altering the electronic store may include, for example,adding, removing, rearranging, re-coloring, highlighting, orde-emphasizing content on the electronic store. Content may include, forexample, advertisement content, product information, information relatedto financial services, directions to the visitor, or combinationsthereof. In some embodiments, the determining includes employing amachine learning model that is trained, via historical customerinteractions, to determine whether, based on the tracked interactions ofthe visitor, whether the visitor is a serious customer or not.

In an exemplary embodiment, training a machine learning model todetermine whether a visitor is a serious customer or not may includeidentifying at least one historical customer that is classified as aserious customer, retrieving tracked interactions of the at least oneidentified historical customer, and training the machine learning modelusing the retrieved tracked interactions as training data and theclassification as ground truth for the classification of the at leastone historical customer. In some embodiments, the machine learning modelemploys a neural network. In some embodiments, the at least onehistorical customer is identified based on one or more of whether the atleast one historical customer obtained a loan or other type of financialprequalification, or whether the at least one historical customercompleted a purchase. In some embodiments, the retrieved trackedinteractions are from respective periods of time prior to when the atleast one historical customer obtained the prequalification and/orcompleted the purchase.

In some embodiments, the training of the machine learning model iscarried out more than once. For example, in some embodiments, a merchantmay, from time to time, make alterations to an electronic store, such asto add, remove, or rearrange content, information, advertising, or thelike. The machine learning model may be retrained in order to accountfor changes to the electronic store. For example, the machine learningmodel may be retrained periodically, on request of a user, in responseto an indication that the electronic store has been modified, or inresponse to any other suitable criterion.

In general, any process discussed in this disclosure that is understoodto be computer-implementable, such as the processes illustrated in FIGS.2 and 3, may be performed by one or more processors of a computersystem, such as computer system 110, as described above. A process orprocess step performed by one or more processors may also be referred toas an operation. The one or more processors may be configured to performsuch processes by having access to instructions (e.g., software orcomputer-readable code) that, when executed by the one or moreprocessors, cause the one or more processors to perform the processes.The instructions may be stored in a memory of the computer system. Aprocessor may be a central processing unit (CPU), a graphics processingunit (GPU), or any suitable type of processing unit.

A computer system, such as computer system 110 and/or user device 150,may include one or more computing devices. If the one or more processorsof the computer system 110 and/or user device 150 are implemented as aplurality of processors, the plurality of processors may be included ina single computing device or distributed among a plurality of computingdevices. If computer system 110 and/or user device 150 comprises aplurality of computing devices, the memory of the computer system 110may include the respective memory of each computing device of theplurality of computing devices.

FIG. 4 is a simplified functional block diagram of a computer 400 thatmay be configured as a device for executing the methods of FIGS. 2 and3, according to exemplary embodiments of the present disclosure. FIG. 4is a simplified functional block diagram of a computer that may beconfigured as the system 110 and/or the user device 150 according toexemplary embodiments of the present disclosure. Specifically, in oneembodiment, any of the mobile devices, systems, servers, etc., discussedherein may be an assembly of hardware 400 including, for example, a datacommunication interface 420 for packet data communication. The platformalso may include a central processing unit (“CPU”) 402, in the form ofone or more processors, for executing program instructions. The platformmay include an internal communication bus 408, and a storage unit 406(such as ROM, HDD, SDD, etc.) that may store data on a computer readablemedium 422, although the system 400 may receive programming and data vianetwork communications. The system 400 may also have a memory 404 (suchas RAM) storing instructions 424 for executing techniques presentedherein, although the instructions 424 may be stored temporarily orpermanently within other modules of system 400 (e.g., processor 402and/or computer readable medium 422). The system 400 also may includeinput and output ports 412 and/or a display 410 to connect with inputand output devices such as keyboards, mice, touchscreens, monitors,displays, etc. The various system functions may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load. Alternatively, the systems may be implemented byappropriate programming of one computer hardware platform.

Instructions executable by one or more processors may be stored on anon-transitory computer-readable medium. Therefore, whenever acomputer-implemented method is described in this disclosure, thisdisclosure shall also be understood as describing a non-transitorycomputer-readable medium storing instructions that, when executed by oneor more processors, cause the one or more processors to perform thecomputer-implemented method. Examples of non-transitorycomputer-readable medium include RAM, ROM, solid-state storage media(e.g., solid state drives), optical storage media (e.g., optical discs),and magnetic storage media (e.g., hard disk drives). A non-transitorycomputer-readable medium may be part of the memory of a computer systemor separate from any computer system.

It should be appreciated that in the above description of exemplaryembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various inventive aspects. This method of disclosure,however, is not to be interpreted as reflecting an intention that theclaims require more features than are expressly recited in each claim.Rather, as the following claims reflect, inventive aspects lie in lessthan all features of a single foregoing disclosed embodiment. Thus, theclaims following the Detailed Description are hereby expresslyincorporated into this Detailed Description, with each claim standing onits own as a separate embodiment of this disclosure.

Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe disclosure, and form different embodiments, as would be understoodby those skilled in the art. For example, in the following claims, anyof the claimed embodiments can be used in any combination.

Thus, while certain embodiments have been described, those skilled inthe art will recognize that other and further modifications may be madethereto without departing from the spirit of the disclosure, and it isintended to claim all such changes and modifications as falling withinthe scope of the disclosure. For example, functionality may be added ordeleted from the block diagrams and operations may be interchanged amongfunctional blocks. Steps may be added or deleted to methods describedwithin the scope of the present disclosure.

The above disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other implementations, which fallwithin the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is tobe determined by the broadest permissible interpretation of thefollowing claims and their equivalents, and shall not be restricted orlimited by the foregoing detailed description. While variousimplementations of the disclosure have been described, it will beapparent to those of ordinary skill in the art that many moreimplementations and implementations are possible within the scope of thedisclosure. Accordingly, the disclosure is not to be restricted.

What is claimed is:
 1. A computer-implemented method comprising:obtaining, via one or more processors, customer identification data andcustomer input data of at least one customer, wherein the customer inputdata comprises a request submitted by the at least one customer;determining, via the one or more processors, a request status of the atleast one customer based on the customer identification data and thecustomer input data; obtaining, via the one or more processors, customerinterface activity data of the at least one customer based on therequest status of the at least one customer; obtaining, via the one ormore processors, customer purchasing data of the at least one customerbased on the request status of the at least one customer; generating,via the one or more processors, a prediction model based on the customerinterface activity data and the customer purchasing data of the at leastone customer; training, via the one or more processors, the generatedprediction model, the training comprising classifying the at least onecustomer based on the customer interface activity data and the customerpurchasing data; obtaining, via the one or more processors, useridentification data and user interface activity data of a user via auser device associated with the user, wherein the user interfaceactivity data indicates one or more interactive activities between theuser and a user interface displayed on the user device; determining, viathe one or more processors, a rating of the user to purchase a productbased on the user identification data, the user interface activity data,and the prediction model; and providing, to the user, an updated userinterface on the user device associated the user based on the determinedrating of the user to purchase the product.
 2. The method of claim 1,wherein the customer identification data includes at least one of aname, a password, or a social security number of the at least onecustomer.
 3. The method of claim 1, wherein the product is a vehicle. 4.The method of claim 1, wherein the customer purchasing data includes atleast one of a confirmation of purchasing the product, a time ofpurchasing the product, or a location of purchasing the product.
 5. Themethod of claim 1, wherein the user is not the at least one customer. 6.The method of claim 1, wherein the customer interface activity dataindicates one or more interactive activities between the at least onecustomer and a customer interface displayed on a customer deviceassociated with the at least one customer.
 7. The method of claim 6,wherein the customer interface activity data further indicates at leasta level of interaction of one of the one or more interactive activitiesbetween the at least one customer and the customer interface displayedon the customer device associated with the at least one customer.
 8. Themethod of claim 1, wherein the one or more interactive activitiesinclude at least one of an action of clicking a link, an action oftyping a search term, or an action of selecting a filter performed bythe user.
 9. The method of claim 1, wherein the user identification dataincludes at least one of a name, a password, or a social security numberof the user.
 10. A computer-implemented method comprising: obtaining,via one or more processors, customer identification data and customerinput data of at least one customer, wherein the customer input datacomprises a prequalification request submitted by the at least onecustomer; determining, via the one or more processors, aprequalification status of the at least one customer based on thecustomer identification data and the customer input data, wherein theprequalification status of the at least one customer identifies the atleast one customer as a prequalified customer; obtaining, via the one ormore processors, customer interface activity data of the at least onecustomer based on the prequalification status of the at least onecustomer; obtaining, via the one or more processors, customer purchasingdata of the at least one customer based on the prequalification statusof the at least one customer; generating, via the one or moreprocessors, a prediction model based on the customer interface activitydata and the customer purchasing data of the at least one customer;training, via the one or more processors, the generated predictionmodel, the training comprising classifying the at least one customerbased on the customer interface activity data and the customerpurchasing data; obtaining, via the one or more processors, useridentification data and user interface activity data of a user via auser device associated with the user, wherein the user interfaceactivity data indicates one or more interactive activities between theuser and a user interface including one or more original layoutsdisplayed on the user device; determining, via the one or moreprocessors, a rating of the user to purchase a product based on the useridentification data, the user interface activity data, and theprediction model; and providing, to the user, an updated user interfaceon the device associated the user based on the rating of the user topurchase the product, wherein the updated user interface includes one ormore adjusted layouts that are different from the one or more originallayouts.
 11. The method of claim 10, wherein the customer identificationdata includes at least one of a name, a password, or a social securitynumber of the at least one customer.
 12. The method of claim 10, whereinthe customer purchasing data includes at least one of a confirmation ofpurchasing the product, a time of purchasing the product, or a locationof purchasing the product.
 13. The method of claim 10, wherein the useris not the at least one customer.
 14. The method of claim 10, whereincustomer interface activity data indicates at least one or moreinteractive activities between the at least one customer and a customerinterface displayed on a customer device associated with the at leastone customer.
 15. The method of claim 14, wherein customer interfaceactivity data further indicates at least a level of interaction of oneof the one or more interactive activities between the at least onecustomer and the customer interface displayed on the customer deviceassociated with the at least one customer.
 16. The method of claim 10,wherein the one or more interactive activities include at least one ofan action of clicking a link, an action of typing a search term, or anaction of selecting a filter performed by the user.
 17. The method ofclaim 10, wherein the user identification data includes at least one ofa name, a password, or a social security number of the user.
 18. Themethod of claim 10, wherein the updated user interface includes at leastone of an updated prequalification interactive component enabling theuser to submit a prequalification request, or an updated search featureenabling the user to search for the product.
 19. The method of claim 10,wherein the prediction model includes a k-nearest neighbors algorithm.20. A computer system, comprising: a memory storing instructions; andone or more processors configured to execute the instructions to performoperations including: obtaining customer identification data andcustomer input data of at least one customer, wherein the customer inputdata comprises a request submitted by the at least one customer;determining a request status of the at least one customer based on thecustomer identification data and the customer input data; obtainingcustomer interface activity data of the at least one customer based onthe request status of the at least one customer; obtaining customerpurchasing data of the at least one customer based on the request statusof the at least one customer; generating a prediction model based on thecustomer interface activity data and the customer purchasing data of theat least one customer; training the generated prediction model, thetraining comprising classifying the at least one customer based on thecustomer interface activity data and the customer purchasing data;obtaining user identification data and user interface activity data ofthe user via a user device associated with a user, wherein the userinterface activity data indicates one or more interactive activitiesbetween the user and a user interface displayed on the user device;determining a rating of the user to purchase a product based on the useridentification data, the user interface activity data, and theprediction model; and providing, to the user, an updated user interfaceon the user device associated the user based on the determined rating ofthe user to purchase the product.